Question 1:

We will use the codes that we’ve learnt in the class for this question. We will first use the gutenbergr package to find our 15 books. then we will lower the cases and count the frequency of the words.
But first we will declare our needed libraries.

#Required Packages:
library(ggplot2)
library(stringr)
library(wordcloud)
library(tm)
library(gutenbergr)
library(tidytext)
library(gsubfn)
library(wordcloud2)
library(ngram)
library(dplyr)
library(highcharter)
library(SnowballC)
library(RColorBrewer)

Now we will run our code to find the mose frequent words in Charles Dickens works:

#HW8-Q1

#---------------------------
#Finding all the books of Charles Dickens:
gutenberg_works(author == "Dickens, Charles")
## # A tibble: 74 x 8
##    gutenberg_id title    author gutenberg_autho~ language gutenberg_books~
##           <int> <chr>    <chr>             <int> <chr>    <chr>           
##  1           46 A Chris~ Dicke~               37 en       Christmas/Child~
##  2           98 A Tale ~ Dicke~               37 en       Historical Fict~
##  3          564 The Mys~ Dicke~               37 en       Mystery Fiction 
##  4          580 The Pic~ Dicke~               37 en       Best Books Ever~
##  5          588 Master ~ Dicke~               37 en       <NA>            
##  6          644 The Hau~ Dicke~               37 en       Christmas       
##  7          650 Picture~ Dicke~               37 en       <NA>            
##  8          653 "The Ch~ Dicke~               37 en       <NA>            
##  9          675 America~ Dicke~               37 en       <NA>            
## 10          678 The Cri~ Dicke~               37 en       Children's Lite~
## # ... with 64 more rows, and 2 more variables: rights <chr>,
## #   has_text <lgl>
#---------------------------
#List of books and their names:
books_list =data.frame(ID= c(580,
               730,
               967,
               700,
               917,
               968,
               821,
               766,
               1023,
               786,
               963,
               98,
               1400,
               883,
               564)  ,Name=
c( "ThePickwickPapers","OliverTwist",
"NicholasNickleby", "TheOldCuriosityShop","BarnabyRudge","MartinChuzzlewit","DombeyandSon",
"DavidCopperfield",
"BleakHouse",
"HardTimes",
"LittleDorrit",
"ATaleofTwoCities",
"GreatExpectations",
"OurMutualFriend",
"TheMysteryofEdwinDrood"))

#---------------------------
# Using the function that we used in class
# This function will fitler the stop words and split the books:
words <- function(i,k)
{
  x = gutenberg_download(k)
  # removing the punctuations:
  x = x %>% 
    str_replace_all("\"","") %>% 
    str_replace_all("[[:punct:]]","") %>% 
    str_split(pattern = "\\s") %>% 
    unlist() %>% 
    table() %>% 
    as.data.frame(stringsAsFactors = F)
  colnames(x) = c("word","count")
  x = x %>%
    filter(!str_to_lower(word) %in% stop_words$word) %>% #filtering stop words
    filter(str_length(word)>1) %>% 
    filter(!str_detect(word,"\\d")) %>%
    mutate(Book = books_list[i,2]) %>% 
    arrange(desc(count)) %>% 
    mutate(proper = !word %in% str_to_lower(word)) 
   dickens_list = x
  return(dickens_list)
}

#---------------------------
#Binding the books:
n = length(books_list$ID)
out = list()

for( i in 1:n)
{
out[[i]] = words(i,books_list[i,1])
}
dickens = bind_rows(out)
dickens$word <- tolower(dickens$word)

#---------------------------
#Sorting: 
dickens %>% 
  group_by(word) %>% 
  summarise(total_count = sum(count)) %>%
  arrange(desc(total_count))-> dickens_sorted
dickens_sorted = dickens_sorted[-c(4),]

#---------------------------
#Plotting the most used words:
dickens_sorted[1:10,] %>% 
  hchart("column",hcaes(x = word, y = total_count)) %>% 
  hc_add_theme(hc_theme_ffx()) %>% 
  hc_yAxis(title =list(text = "Total Number of Repitions"))
#---------------------------

Question 2:

We will use the wordcloud2 package. From the last question we found the top frequent words which are not in the stop_words. We will use the given image in the home work to plot the wordcloud of the top 200 most frequent words.

#HW8-Q2:

#---------------------------
#Sorting:
dickens_sorted %>% 
  select(word = word, freq = total_count) -> dickens_sorted_plot
#---------------------------
#wordcloud2 library(we will download it again to ensure it plots in rmarkdown)
library(devtools)
devtools::install_github("lchiffon/wordcloud2")
library(wordcloud2)
#---------------------------
#Plotting All of them:
wordcloud2(dickens_sorted_plot,size = 0.2,
           figPath = "/Users/shervin/Desktop/untitled folder 3/Unknown.png")
#---------------------------
# The first 200:
wordworth = dickens_sorted_plot[1:200,]
wordcloud2(wordworth,size = 0.1,
            figPath = "/Users/shervin/Desktop/untitled folder 3/Unknown.png")

Question 3:

In this question we will find the main characters by finding all the upper case words and checking whether in the books exists any lower case of these words. If the lower case also exists therefore it is not a character and thus we can exclude them. The remaining words are the characters forename and surname. As we can’t have a specific way to differentiate if it is the surname fo forename therefore we may exclude the assumption that we are looking only for the name.Therefore in this question we found the names and the surnames.

#HW8-3:

#---------------------------
#Declaring a function which will find the main characters:
main_char <- function(i){

value = as.data.frame(out[i]) # "out" from the first question: words of every book
unique = unique(value)
unique_lower = unique[grep("^[a-z]", unique$word),] # all the lower case unique words


temp = value[grep("^[A-Z]", value$word),]
temp$word <- tolower(temp$word)
index =which(temp$word =="sir")
if(length(index)!=0)
temp = temp[-index,]

for (k in  1:100)
{
  if(temp$word[k] %in% unique_lower$word  )
    temp = temp[-k,]
}
return(temp$word[1:5])
}

#---------------------------
#Finding the main characters of the 15 books:
output = list()
for ( i in 1:15)
{
 output[[i]] = main_char(i)
}
main_characters = output

main_characters2 <- sapply(main_characters, tolower)

#---------------------------
#Finding the number of times the names have come in the books:
dickens %>%
  filter(word %in% main_characters2 ) %>% 
  group_by(Book) %>% 
  mutate(percent = round(100*count/sum(count))) %>% 
  hchart("column",hcaes(x = Book, y = percent, group = word)) %>% 
  hc_add_theme(hc_theme_ffx())
#---------------------------

Question 4:

We will use the sentiments package to find the necessary words.

#HW8-Q4:

a = list()
for (i in 1:15)
{
  
  book = out[[i]]
  book$word <- tolower(book$word)  
  book %>% group_by(word) %>% 
    summarise(count = sum(count)) %>% 
    arrange(desc(count)) -> book_count
  
  book_count %>% 
    mutate( is.sentiment = (word %in% sentiments$word) ) -> book_sentiment
  
  book_sentiment %>% 
    filter(is.sentiment == TRUE) -> book_sentiment
  row_sentiment = length(book_sentiment$word)
  
  for( j in 1:row_sentiment)
  {
    book_sentiment$type[j] = sentiments$sentiment[which(
                             sentiments$word==book_sentiment$word[j])]
    
  }
  book_sentiment = na.omit(book_sentiment)
  book_sentiment %>% 
    select(word,count,type) %>% 
    filter(type == "positive") -> book_sentiment_p
  
  book_sentiment %>% 
    select(word,count,type) %>% 
    filter(type == "negative") -> book_sentiment_n
  
  
  binding = rbind(book_sentiment_p[1:20,],book_sentiment_n[1:20,])
   
  
  
  layout(matrix(c(1, 2), nrow=2), heights=c(2, 3))
par(mar=rep(0, 4))  
plot.new()
text(x=0.5, y=0.01, sprintf("Negative vs Positive of the book %s",books_list[i,2]))
wordcloud(binding$word,binding$count,
          random.order=FALSE, rot.per=0.1, 
          ordered.colors=TRUE,
          colors=brewer.pal(3, "Dark2")[factor(binding$type)],
          main="Title",
          scale=c(8,.3))
name = factor(factor(binding$type),levels(factor(binding$type))[c(2,1)])
legend("topright",
       legend = levels(factor(binding$type)),
       text.col=brewer.pal(8, "Dark2")[unique(name)])
  
  book_sentiment %>% 
    select(word,count,type) %>% 
    filter( !(type %in% c("negative","positive"))) -> book_sentiment_feel
  
  
  hc_sentiment <- highchart() %>% 
  hc_add_series(name = "frequency of two words",data = book_sentiment_feel[1:8,],
                type = "bar",
                mapping = hcaes(x = word, y = count),
                colorByPoint = TRUE) %>% 
  hc_xAxis(title = list(text = "Name of the  Words"),
           categories = book_sentiment_feel$word) %>% 
  hc_yAxis(title = list(text = "Frequency")) %>% 
  hc_title(text =sprintf("Sentiment in %s",books_list[i,2]))



a[[i]] = hc_sentiment
  
}

htmltools::tagList(a)

Qustion 5:

In this question question we will define some function to find the negative and positive sentiments and divide the Les Miserables to 200 pieces.

#HW8 -Q5:

#---------------------------
# Fundning to sentiments of negative and positive:
sentiments %>% 
  filter(sentiment ==c("negative")) -> negative_sentiments #negative

sentiments %>% 
  filter(sentiment ==c("positive")) -> positive_sentiments #positive

#---------------------------
#Finding works of Victor Hugo:
Hugo = gutenberg_works(author == "Hugo, Victor")


words <- function(i,k)
{
  x = gutenberg_download(k)
  x = x %>% 
    str_replace_all("\"","") %>% 
    str_replace_all("[[:punct:]]","") %>% 
    str_split(pattern = "\\s") %>% 
    unlist() %>% 
    table() %>% 
    as.data.frame(stringsAsFactors = F)
  colnames(x) = c("word","count")
  x = x %>%
    filter(!str_to_lower(word) %in% stop_words$word) %>% 
    filter(str_length(word)>1) %>% 
    filter(!str_detect(word,"\\d")) %>%
    mutate(Book = books_list[i,2]) %>% 
    arrange(desc(count)) %>% 
    mutate(proper = !word %in% str_to_lower(word)) 
   dickens_list = x
  return(dickens_list)
}
#Listing the 5 books of Les Miserables:
hugo_books_list = c(48731:48735)
m = length(hugo_books_list)
check = list()
for( i in 1:m)
{
  check[[i]] = words(i,hugo_books_list[i])
}

books_split_hugo = list()
for (i in 1:m)
{
h = gutenberg_download(hugo_books_list[i])

books_split_hugo[[i]] =str_split(h[,2],boundary("word"))
}
books_split_hugo_unlist = unlist(books_split_hugo)

#---------------------------
#Deviding the whole book to 200 pieces:
sample = list()
g = length(books_split_hugo_unlist)
for(i in 1:200 )
{
  sample[[i]] = books_split_hugo_unlist[1+ceiling((i-1)*(g/200)):ceiling((i*g/200))]
}

#---------------------------
#Function which will find the number of positive sentiments:
sentiments_positive <- function(i)
{
    
    sample_data = as.data.frame(sample[[i]])
    sample_data_words = sample[[i]]
    sample_data <- sapply(sample_data,tolower)
    
    sample_data_words = sample_data_words %>% 
      str_replace_all("\"","") %>% 
      str_replace_all("[[:punct:]]","") %>% 
      str_split(pattern = "\\s") %>% 
      unlist() %>% 
      table() %>% 
      as.data.frame(stringsAsFactors = F)
    colnames(sample_data_words) = c("word","count")
    sample_data_words = sample_data_words %>%
      filter(!str_to_lower(word) %in% stop_words$word) %>% 
      filter(str_length(word)>1) %>% 
      filter(!str_detect(word,"\\d")) %>%
      arrange(desc(count)) %>% 
      mutate(proper = !word %in% str_to_lower(word)) 
      hugo = sample_data_words
      hugo %>% 
        mutate(is.negative = (hugo$word %in% negative_sentiments$word)) -> hugo_negative
      hugo_negative %>% 
        group_by(is.negative) %>% 
        summarise(count = sum(count)) -> hugo_negative_count
      
      hugo %>% 
        mutate(is.positive = (hugo$word %in% positive_sentiments$word)) -> hugo_positive
      hugo_positive %>% 
        group_by(is.positive) %>% 
        summarise(count = sum(count)) -> hugo_positive_count
      return(hugo_positive_count$count[2])
}
#---------------------------
#Function which will find the number of negative sentiments:
sentiments_negative <- function(i)
{
  
  sample_data = as.data.frame(sample[[i]])
  sample_data_words = sample[[i]]
  sample_data <- sapply(sample_data,tolower)
  
  sample_data_words = sample_data_words %>% 
    str_replace_all("\"","") %>% 
    str_replace_all("[[:punct:]]","") %>% 
    str_split(pattern = "\\s") %>% 
    unlist() %>% 
    table() %>% 
    as.data.frame(stringsAsFactors = F)
  colnames(sample_data_words) = c("word","count")
  sample_data_words = sample_data_words %>%
    filter(!str_to_lower(word) %in% stop_words$word) %>% 
    filter(str_length(word)>1) %>% 
    filter(!str_detect(word,"\\d")) %>%
    arrange(desc(count)) %>% 
    mutate(proper = !word %in% str_to_lower(word)) 
  hugo = sample_data_words
  hugo %>% 
    mutate(is.negative = (hugo$word %in% negative_sentiments$word)) -> hugo_negative
  hugo_negative %>% 
    group_by(is.negative) %>% 
    summarise(count = sum(count)) -> hugo_negative_count
  
  return(hugo_negative_count$count[2])

}
#---------------------------
#Finding the number of Positive and Negatives:
positive = c(1:200)
negative = c(1:200)

for (i in 1:200)
{
  positive[i] = sentiments_positive(i)
  negative[i] = sentiments_negative(i)
  
}

#---------------------------
#Plotting the values:
plot(negative,type="line", xlab = "Piece Number", ylab = "Frequency", main="Neagtive words")

plot(positive, type= "line",xlab = "Piece Number", ylab = "Frequency", main ="Positive")

hc <- highchart() %>% 
  hc_add_series(name ="Negative",
                type ="area",
                data =negative) %>% 
  hc_add_series(name = "Positive",
                type = "area",
                data = positive) %>% 
  hc_xAxis(title = list(text = "Piece Number")) %>% 
  hc_yAxis(title = list(text = "Frequency")) %>% 
  hc_title(text =sprintf("Positive-Negative in Les Miserables divided in 200 pieces"))
hc
#---------------------------

Question 6:

We will find the two words using the ngram package. With this package we can find every n consecutive words.

#HW8-Q6:
#---------------------------
#We will first extract the books:
hug = gutenberg_download(48731:48735)

#---------------------------
#Now we will remove the uncessary punctuations and give a sign for the places
#that have ", . ! @" this sign is "!!!!!" we will need this sign:

hugs = hug %>%  
  str_replace_all("\"","") %>% 
  str_replace_all("[[:punct:]]"," \\!!!!!")
hugs <- sapply(hugs,tolower)
#--------------------------- 
#Using the N-grqm package we will find the 2 word:
ng <- ngram(hugs,n=2) #ng object
ngram = get.phrasetable(ng)
ngrams = as.data.frame(ngram) #making it as a data frame

#--------------------------- 
# length or ngram:
J = length(ngrams$ngrams)

#--------------------------- 
# we will find those that have punctuations using the statment 
# we said in the upper lines:
check_ngram = str_extract(ngrams$ngrams,"\\!") # removing punctuations
indexes = which(check_ngram==c("!")) # indexes of those we dont want

#--------------------------- 
# Removed indesxes:
ngrams2 = ngram[-indexes,]
ngrams2$ngrams = sapply(ngrams2$ngrams,tolower)
#--------------------------- 
# Now we will remove the stop words:
for(k in 1:50)
{
  for ( i in 1:100)
  {
    tempe =ngrams2$ngrams[i]
    tempe_split = str_split(tempe," ")
    tempe_split = unlist(tempe_split)
    if(sum(tempe_split %in% stop_words$word))
      ngrams2 = ngrams2[-i,]
  }
}

#----------------------------------------
#Plotting the most frequent 30 words:
ngrams2 = as.data.frame(ngrams2)

hc_two_words <- highchart() %>% 
  hc_add_series(name = "frequency of two words",data = ngrams2[1:30,],
                type = "column",
                mapping = hcaes(x = ngrams, y = freq),
                color = "navy blue") %>% 
  hc_xAxis(title = list(text = "Name of the Two Words"),
           categories = ngrams2$ngrams) %>% 
  hc_yAxis(title = list(text = "Frequency")) %>% 
  hc_title(text ="Frequency of two words in Les Miserables")
hc_two_words  
#----------------------------------------

Question 7:

We use the ngram package and the result of the last question to find the words that start with he and she and with these combinationats we will find the most used verbs.

#HW8-Q7:
#---------------------------
#We will first extract the books:
dickens_charles = gutenberg_download(books_list[,1])

#---------------------------
#Now we will remove the uncessary punctuations and give a sign for the places
#that have ", . ! @" this sign is "!!!!!" we will need this sign:

dickens_charles_str = dickens_charles %>%  
  str_replace_all("\"","") %>% 
  str_replace_all("[[:punct:]]"," \\!!!!!")
dickens_charles_str <- sapply(dickens_charles_str,tolower)
#--------------------------- 
#Using the N-grqm package we will find the 2 word:
ng <- ngram(dickens_charles_str,n=2) #ng object
ngram = get.phrasetable(ng)
ngrams = as.data.frame(ngram) #making it as a data frame

#--------------------------- 
# length or ngram:
J = length(ngrams$ngrams)

#--------------------------- 
# we will find those that have punctuations using the statment 
# we said in the upper lines:
check_ngram = str_extract(ngrams$ngrams,"\\!") # removing punctuations
indexes = which(check_ngram==c("!")) # indexes of those we dont want

#--------------------------- 
# Removed indesxes:
ngrams_dickens = ngram[-indexes,]
J2 = length(ngrams_dickens$ngrams)
ngrams_dickens$ngrams = sapply(ngrams_dickens$ngrams,tolower)
#--------------------------- 
# Now we will find the words that start with he/she:
rows_dickens = nrow(ngrams_dickens)
index_dickens = list()
b = 1
for(i in 1:rows_dickens)
{
  tempe =ngrams_dickens$ngrams[i]
  tempe_split = str_split(tempe," ")
  tempe_split = unlist(tempe_split)
  if(sum(tempe_split[1] %in% c("he","she")) )
  {
    index_dickens[b] = i
    b = b+1
  }
}
#--------------------------- 
# Those that start with he or she:
index_dickens = unlist(index_dickens)
ngrams_dickens_he_she = ngrams_dickens[index_dickens,]
ngrams_dickens_he_she
##                        ngrams freq         prop
## 82                    he had  5724 9.517839e-04
## 94                    he was  4679 7.780218e-04
## 205                  she was  2102 3.495195e-04
## 210                  she had  2056 3.418707e-04
## 292                  he said  1498 2.490867e-04
## 295                    he is  1486 2.470914e-04
## 312                 he would  1402 2.331239e-04
## 358                 he could  1228 2.041912e-04
## 539                   he has   868 1.443306e-04
## 789                she would   623 1.035921e-04
## 790                 she said   623 1.035921e-04
## 859                 he might   577 9.594328e-05
## 869                   he did   573 9.527816e-05
## 872                   she is   571 9.494560e-05
## 959                he looked   524 8.713046e-05
## 964                  he were   522 8.679791e-05
## 1051                 he took   474 7.881649e-05
## 1060                 he went   470 7.815137e-05
## 1098               she could   454 7.549090e-05
## 1150               he should   430 7.150019e-05
## 1420                 he came   358 5.952807e-05
## 1445                 she has   354 5.886295e-05
## 1451                 he knew   353 5.869667e-05
## 1466                  he saw   349 5.803155e-05
## 1587                 he made   322 5.354200e-05
## 1601              he thought   319 5.304316e-05
## 1709                  he sat   300 4.988385e-05
## 1754                 he will   291 4.838734e-05
## 1755                 he must   291 4.838734e-05
## 1811                he spoke   284 4.722338e-05
## 1918                 he felt   270 4.489547e-05
## 1935                he never   267 4.439663e-05
## 1939                 she did   267 4.439663e-05
## 1953               he turned   265 4.406407e-05
## 1991                he stood   260 4.323267e-05
## 2089                 she say   248 4.123732e-05
## 2124             he returned   244 4.057220e-05
## 2366                he found   220 3.658149e-05
## 2380              she looked   219 3.641521e-05
## 2454                he added   212 3.525126e-05
## 2464               she might   211 3.508498e-05
## 2545                  he may   205 3.408730e-05
## 2577               he seemed   203 3.375474e-05
## 2628                she were   198 3.292334e-05
## 2790                  he put   188 3.126055e-05
## 2852                  he can   184 3.059543e-05
## 2928                  he got   180 2.993031e-05
## 3106                she took   170 2.826752e-05
## 3167                  he and   167 2.776868e-05
## 3205                 he gave   165 2.743612e-05
## 3226                he began   164 2.726984e-05
## 3269                 she sat   162 2.693728e-05
## 3296                she went   160 2.660472e-05
## 3384                he asked   156 2.593960e-05
## 3409                 she saw   155 2.577332e-05
## 3419              she should   154 2.560704e-05
## 3524              he stopped   150 2.494193e-05
## 3666                 he didn   145 2.411053e-05
## 3710             he answered   143 2.377797e-05
## 3714              he replied   143 2.377797e-05
## 3794                she came   140 2.327913e-05
## 3798            she answered   140 2.327913e-05
## 3840                she knew   138 2.294657e-05
## 3869                 he told   137 2.278029e-05
## 3877                he heard   137 2.278029e-05
## 3926               he walked   135 2.244773e-05
## 3929                 he does   135 2.244773e-05
## 3934                he knows   135 2.244773e-05
## 4086                 he then   130 2.161634e-05
## 4290            she returned   123 2.045238e-05
## 4319                 he held   123 2.045238e-05
## 4344                 he left   122 2.028610e-05
## 4462             she thought   119 1.978726e-05
## 4537                he comes   117 1.945470e-05
## 4541                she will   117 1.945470e-05
## 4661               she never   114 1.895586e-05
## 4696                she made   114 1.895586e-05
## 4922                 he fell   109 1.812447e-05
## 5069               he passed   106 1.762563e-05
## 5148                 he puts   104 1.729307e-05
## 5184                 he drew   103 1.712679e-05
## 5308               he couldn   101 1.679423e-05
## 5330               he wanted   101 1.679423e-05
## 5442                he meant    98 1.629539e-05
## 5463                she must    98 1.629539e-05
## 5575                she gave    96 1.596283e-05
## 5633                  he don    95 1.579655e-05
## 5735                he cried    93 1.546399e-05
## 5815                she felt    91 1.513144e-05
## 5921               she spoke    90 1.496516e-05
## 5932               he always    90 1.496516e-05
## 5939                he shook    90 1.496516e-05
## 5964                  he now    89 1.479888e-05
## 5982              she turned    89 1.479888e-05
## 5990               she asked    89 1.479888e-05
## 6002                 he wore    89 1.479888e-05
## 6008               he called    89 1.479888e-05
## 6151              she seemed    87 1.446632e-05
## 6189                 he laid    86 1.430004e-05
## 6220               she stood    86 1.430004e-05
## 6317               he wouldn    84 1.396748e-05
## 6474               he became    82 1.363492e-05
## 6567                 he ever    81 1.346864e-05
## 6692                 she and    79 1.313608e-05
## 6694             he appeared    79 1.313608e-05
## 6804                 he used    78 1.296980e-05
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## 640628                he von     1 1.662795e-07
## 640942         he delicately     1 1.662795e-07
## 641070            he visited     1 1.662795e-07
## 641144           he reverted     1 1.662795e-07
## 641344            he detects     1 1.662795e-07
## 641500          he resembled     1 1.662795e-07
## 642579             he calmly     1 1.662795e-07
## 642598           he stealeth     1 1.662795e-07
## 642856       he interpolates     1 1.662795e-07
## 642912             he openly     1 1.662795e-07
## 643708        she inevitably     1 1.662795e-07
## 644043            he decides     1 1.662795e-07
## 644278            she scarce     1 1.662795e-07
## 644325             he escape     1 1.662795e-07
## 644581         she conversed     1 1.662795e-07
## 644923                she up     1 1.662795e-07
## 644928            she termed     1 1.662795e-07
## 645026               he flew     1 1.662795e-07
## 645403              he sorry     1 1.662795e-07
## 645613            he anoints     1 1.662795e-07
## 645689    he philosophically     1 1.662795e-07
## 646136         she lightened     1 1.662795e-07
## 646289            she brewed     1 1.662795e-07
## 646529     he been<u+2014>so     1 1.662795e-07
## 646801            he presses     1 1.662795e-07
## 647615          she advances     1 1.662795e-07
## 647808       he benevolently     1 1.662795e-07
## 648090             she stuck     1 1.662795e-07
## 648108          she acquired     1 1.662795e-07
## 648181           she summons     1 1.662795e-07
## 648205             he biting     1 1.662795e-07
## 648392          she designed     1 1.662795e-07
## 648610          he emigrated     1 1.662795e-07
## 648662           he sturdily     1 1.662795e-07
## 648693            he insured     1 1.662795e-07
## 648695           she courted     1 1.662795e-07
## 648815       he corroborated     1 1.662795e-07
## 648830          she flounced     1 1.662795e-07
## 649201         she disclosed     1 1.662795e-07
## 649264         she disgraced     1 1.662795e-07
## 649309          she searched     1 1.662795e-07
## 649432         she sustained     1 1.662795e-07
## 649801             he defers     1 1.662795e-07
## 649996            he deputed     1 1.662795e-07
## 650291             he shared     1 1.662795e-07
## 651214           he consults     1 1.662795e-07
## 651294 he is<u+2014><u+2019>     1 1.662795e-07
## 651590              he drily     1 1.662795e-07
## 651665             he prized     1 1.662795e-07
## 651866           she clapped     1 1.662795e-07
## 652088          she murmured     1 1.662795e-07
## 652233           he hardened     1 1.662795e-07
## 652683            he divests     1 1.662795e-07
## 652787           she starved     1 1.662795e-07
## 652922         he recruiting     1 1.662795e-07
## 652959              he order     1 1.662795e-07
## 653880          she overcame     1 1.662795e-07
## 653912         he thankfully     1 1.662795e-07
## 653998             she cause     1 1.662795e-07
## 654545          she presumes     1 1.662795e-07
## 654689           he inserted     1 1.662795e-07
## 654692            he tarries     1 1.662795e-07
## 654876            she heeded     1 1.662795e-07
## 654912               she try     1 1.662795e-07
## 655062             he gladly     1 1.662795e-07
## 655171             she there     1 1.662795e-07
## 655332            he tangles     1 1.662795e-07
## 655465             he clasps     1 1.662795e-07
## 655654        she associated     1 1.662795e-07
## 655688           he perfects     1 1.662795e-07
## 656085       he reconnoitred     1 1.662795e-07
## 656187             he thawed     1 1.662795e-07
## 656474           he highsted     1 1.662795e-07
## 656892              he stand     1 1.662795e-07
## 656996         he calculated     1 1.662795e-07
## 657111            he unwinds     1 1.662795e-07
## 657132            he crashed     1 1.662795e-07
## 657173           she falters     1 1.662795e-07
## 657331       he incidentally     1 1.662795e-07
## 657752            she shaded     1 1.662795e-07
## 659230          she pretends     1 1.662795e-07
## 659350           she hitched     1 1.662795e-07
## 659653             he mildly     1 1.662795e-07
## 659715            he farther     1 1.662795e-07
## 659921          he signifies     1 1.662795e-07
## 659959            he charges     1 1.662795e-07
## 660673              she owes     1 1.662795e-07
## 661163          she wilfully     1 1.662795e-07
## 661300        she disappears     1 1.662795e-07
## 661584             he shrank     1 1.662795e-07
## 661679             he spared     1 1.662795e-07
## 662911              he hints     1 1.662795e-07
## 662954          she mentally     1 1.662795e-07
## 663299            she asleep     1 1.662795e-07
## 664309         he handsomely     1 1.662795e-07
## 664781          she honoured     1 1.662795e-07
## 665378            she quoted     1 1.662795e-07
## 665643            he departs     1 1.662795e-07
## 665720           he combines     1 1.662795e-07
## 665973         she condemned     1 1.662795e-07
## 666124               he spun     1 1.662795e-07
## 666193            he cutting     1 1.662795e-07
## 666199            she elopes     1 1.662795e-07
## 666548             he swings     1 1.662795e-07
## 666781         he reconciled     1 1.662795e-07
## 667187         he subscribed     1 1.662795e-07
## 667191               he jerk     1 1.662795e-07
## 667361          she squeezed     1 1.662795e-07
## 667480           he differed     1 1.662795e-07
## 667735           she prefers     1 1.662795e-07
## 668068          he interrupt     1 1.662795e-07
## 668371         she concealed     1 1.662795e-07
## 668643           she acquits     1 1.662795e-07
## 668875         he misdoubted     1 1.662795e-07
## 669268             he asleep     1 1.662795e-07
## 669466            he wrested     1 1.662795e-07
## 669485              he rally     1 1.662795e-07
## 669677             he repaid     1 1.662795e-07
## 669847             he stones     1 1.662795e-07
## 669876            he foresee     1 1.662795e-07
## 669900          she extended     1 1.662795e-07
## 670272                he kep     1 1.662795e-07
## 670275        she marshalled     1 1.662795e-07
## 670403              he fades     1 1.662795e-07
## 670490               he love     1 1.662795e-07
## 670727              he spent     1 1.662795e-07
## 671287          she forgives     1 1.662795e-07
## 671635            he stiffly     1 1.662795e-07
## 671646           he complies     1 1.662795e-07
## 671925        she supervised     1 1.662795e-07
## 673273           he behaving     1 1.662795e-07
## 673581           he bespeaks     1 1.662795e-07
## 673763          she declares     1 1.662795e-07
## 673885       he accidentally     1 1.662795e-07
## 673897           he unpacked     1 1.662795e-07
## 674194         she dutifully     1 1.662795e-07
## 674567          she reasoned     1 1.662795e-07
## 675122               he many     1 1.662795e-07
## 675153            he spilled     1 1.662795e-07
## 675275            he exhorts     1 1.662795e-07
## 675492            she freely     1 1.662795e-07
## 675666         he squandered     1 1.662795e-07
## 675678         he personally     1 1.662795e-07
## 675750              he angry     1 1.662795e-07
## 676039            he induced     1 1.662795e-07
## 676199            she kisses     1 1.662795e-07
## 676291            she mashed     1 1.662795e-07
## 676419             she worth     1 1.662795e-07
## 676515          he suspended     1 1.662795e-07
## 676679           he employed     1 1.662795e-07
## 676697          he scrambled     1 1.662795e-07
## 676833           she dragged     1 1.662795e-07
## 677006         he occasioned     1 1.662795e-07
## 677148          she deserved     1 1.662795e-07
## 677776           she finally     1 1.662795e-07
## 677984          she lamented     1 1.662795e-07
## 678116            she rouged     1 1.662795e-07
## 678253            she undrew     1 1.662795e-07
## 678714            she dozing     1 1.662795e-07
## 678940            he grubbed     1 1.662795e-07
## 678973          she replaced     1 1.662795e-07
## 679305           she favored     1 1.662795e-07
## 679408             he amuses     1 1.662795e-07
## 679535             she grins     1 1.662795e-07
## 679854        he perpetrated     1 1.662795e-07
## 679996           she stirred     1 1.662795e-07
## 680176            he existed     1 1.662795e-07
## 680411      she can<u+2019>t     1 1.662795e-07
## 680618             she tires     1 1.662795e-07
## 680638               he bows     1 1.662795e-07
## 680780          she suspects     1 1.662795e-07
## 681708              he halts     1 1.662795e-07
## 681711         she threatens     1 1.662795e-07
## 681730          he frightens     1 1.662795e-07
## 681802      she incoherently     1 1.662795e-07
## 681888           he polished     1 1.662795e-07
## 682469         she slackened     1 1.662795e-07
## 682630          he postponed     1 1.662795e-07
## 682635             he fawned     1 1.662795e-07
## 682654               he such     1 1.662795e-07
## 683080       he straightened     1 1.662795e-07
## 683198             he bawled     1 1.662795e-07
## 683259           he strapped     1 1.662795e-07
## 683307              he known     1 1.662795e-07
## 683330           she replead     1 1.662795e-07
## 683602          he dispenses     1 1.662795e-07
## 683869              he gazes     1 1.662795e-07
## 684278           he scatters     1 1.662795e-07
## 684915           he darkened     1 1.662795e-07
## 685146           he lighting     1 1.662795e-07
## 685397             he enters     1 1.662795e-07
## 685875         he doubtingly     1 1.662795e-07
## 686147          he refrained     1 1.662795e-07
## 686245               he eyes     1 1.662795e-07
## 686312              she bows     1 1.662795e-07
## 686426          he prolonged     1 1.662795e-07
## 686580           he knuckled     1 1.662795e-07
## 686753           she mingled     1 1.662795e-07
## 686926         she contrived     1 1.662795e-07
## 687222            he relents     1 1.662795e-07
## 687291           he outlived     1 1.662795e-07
## 687321                he mad     1 1.662795e-07
## 687414           he overcame     1 1.662795e-07
## 687612           he reproved     1 1.662795e-07
## 687640         she retouches     1 1.662795e-07
## 687713           he mingling     1 1.662795e-07
## 687784           she singing     1 1.662795e-07
## 687965       he disinherited     1 1.662795e-07
## 687974           she charged     1 1.662795e-07
## 687992            he courted     1 1.662795e-07
## 688464         he blinkingly     1 1.662795e-07
## 688715            she fanned     1 1.662795e-07
## 688771            he resting     1 1.662795e-07
## 688862            he soothed     1 1.662795e-07
## 689207          she troubled     1 1.662795e-07
## 689395           he condoled     1 1.662795e-07
## 689464              he names     1 1.662795e-07
## 689492              he bends     1 1.662795e-07
## 689507            he depicts     1 1.662795e-07
## 689670    he condescendingly     1 1.662795e-07
## 689813           he prepares     1 1.662795e-07
## 690173         she condensed     1 1.662795e-07
rows_he_she = nrow(ngrams_dickens_he_she)
for( k in 1:10)
{
  for ( i in 1:500)
  {
    tempe =ngrams_dickens_he_she$ngrams[i]
    tempe_split = str_split(tempe," ")
    tempe_split = unlist(tempe_split)
    if(sum(tempe_split[2] %in% stop_words$word))
      ngrams_dickens_he_she = ngrams_dickens_he_she[-i,]
  }
}
ngrams_dickens_he_she = ngrams_dickens_he_she[-11,]

#--------------------------- 
# Plotting the verbs with he/she:

hc_he_she <- highchart() %>% 
  hc_add_series(name = "frequency of verbs",data = ngrams_dickens_he_she[1:30,],
                type = "column",
                mapping = hcaes(x = ngrams, y = freq),
                color = "navy blue") %>% 
  hc_xAxis(title = list(text = "Name of the Two Words"),
           categories = ngrams_dickens_he_she$ngrams) %>% 
  hc_yAxis(title = list(text = "Frequency")) %>% 
  hc_title(text ="Frequency of verbs in Les Miserables")

hc_he_she 
#--------------------------- 
#Now we will find the verbs:
ngrams_dickens_he_she_new = ngrams_dickens_he_she
verbs_rows = nrow(ngrams_dickens_he_she)
for( i in 1:verbs_rows)
{
  tempe =ngrams_dickens_he_she$ngrams[i]
  tempe_split = str_split(tempe," ")
  tempe_split = unlist(tempe_split)
  ngrams_dickens_he_she_new$ngrams[i] = tempe_split[2]
  
}
ngrams_dickens_he_she_new = as.data.frame(ngrams_dickens_he_she_new)
ngrams_dickens_he_she_new %>% 
  group_by(ngrams) %>% 
  summarise(freq = sum(freq)) %>% 
  arrange(desc(freq))-> ngrams_dickens_he_she_new_count

#--------------------------- 
#Plotting the verbs:
hc_he_she_new <- highchart() %>% 
  hc_add_series(name = "frequency of verbs",data = ngrams_dickens_he_she_new_count[1:30,],
                type = "column",
                mapping = hcaes(x = ngrams, y = freq),
                color = "blue") %>% 
  hc_xAxis(title = list(text = "Name of the Verbs"),
           categories = ngrams_dickens_he_she_new_count$ngrams) %>% 
  hc_yAxis(title = list(text = "Frequency")) %>% 
  hc_title(text ="Frequency of verbs in Charles Dickens works")

hc_he_she_new

Question 8:

Using the ngram pacakge we will first find the 1-gram and bigram of the chapters. We found the chapters by spliting the book in places that have CHAPTER. We removed the contents page. We plotted for the Oliver Twist book because there will be many many plots ( more than 140 plots) for all of the books. But we will find the n-gram distribution for all the books. We could also use chi-square test but these graphs give us efficient information to.

#HW8-Q8:
d = list()
c = list()
#---------------------------
#Collectiing the book and cleaning it:
Oliver_Twist = gutenberg_download(730)
Oliver_Twist[[2]][1:117] <- " " #removing contents page

Oliver_Twist_new = Oliver_Twist %>%  
  str_replace_all("\"","") %>% 
  str_replace_all("[[:punct:]]","") 
chapters =str_split(Oliver_Twist_new, "CHAPTER")
#---------------------------
# Extracting the 1-grams for every chapter:
r_2 =length(chapters[[2]])
ngram_dickens = list()
index = list()
for (i in 2:r_2)
{
  k = chapters[[2]][i] %>%
    str_to_lower() %>% 
    str_split(pattern = "\\s") %>% 
    unlist() %>% 
    table() %>% 
    as.data.frame(stringsAsFactors = F)
  colnames(k) = c("word","count")
  
  k = k %>%
    filter(!str_to_lower(word) %in% stop_words$word) %>% 
    filter(str_length(word)>1) %>% 
    filter(!str_detect(word,"\\d")) %>%
    arrange(desc(count)) %>% 
    mutate(proper = !word %in% str_to_lower(word))
  
  hc_chapter <- highchart() %>% 
    hc_add_series(name = "frequency of single words",data = k[1:50,],
                  type = "column",
                  mapping = hcaes(x = word, y = count),
                  color = "blue") %>% 
    hc_xAxis(title = list(text = "Name of the Words"),
             categories = k$word) %>% 
    hc_yAxis(title = list(text = "Frequency")) %>% 
    hc_title(text =sprintf("Frequency of words in Oliver Twist, Chapter %s"
                           ,i))
  
  
d[[i]] = hc_chapter
  
}


#---------------------------
#Extracting 2-grams for every chapter:
ngram_dickens = list()
r_2 =length(chapters[[2]])
index = list()


for (i in 1:r_2)
{
  b = 0 
  ngram_dickens = chapters[[2]][i] %>%
    str_to_lower()
  ngram_dickens= get.phrasetable(ngram(ngram_dickens,n=2))
  ngram_dickens_dataframe_2= as.data.frame(ngram_dickens)
  
  
  
  hc_chapter_2 <- highchart() %>% 
    hc_add_series(name = "frequency of bigrams",data = ngram_dickens_dataframe_2[1:50,],
                  type = "column",
                  mapping = hcaes(x = ngrams, y = freq),
                  color = "blue") %>% 
    hc_xAxis(title = list(text = "Name of the Words"),
             categories = ngram_dickens_dataframe_2$ngrams) %>% 
    hc_yAxis(title = list(text = "Frequency")) %>% 
    hc_title(text =sprintf("Frequency of bigram in Oliver Twist, Chapter %s"
                           ,i))
  c[[i]] = hc_chapter_2
}




#---------------------------
htmltools::tagList(c)
htmltools::tagList(d)

This was for the first part. Now will find the n-gram distribution of charles dickens books. We will use the logarithm of frequencies

#------
#ngram of charles dickens books:
rows = nrow(books_list)
for (i in 1:rows )
{
book_charles =gutenberg_download(books_list[i,1])

book_charles = book_charles %>%  
  str_replace_all("\"","") %>% 
  str_replace_all("[[:punct:]]","\\!!!!!!")
book_charles <- sapply(book_charles,tolower)



ng <-ngram(book_charles,n=1)
ngram_1 = get.phrasetable(ng)
check = str_extract(ngram_1$ngrams,"\\!") 
indexes = which(check==c("!"))
ngram_1 = ngram_1[-indexes,]

ng <-ngram(book_charles,n=2)
ngram_2 = get.phrasetable(ng)
check = str_extract(ngram_2$ngrams,"\\!") 
indexes = which(check==c("!"))
ngram_2 = ngram_2[-indexes,]


ng <-ngram(book_charles,n=3)
ngram_3 = get.phrasetable(ng)
check = str_extract(ngram_3$ngrams,"\\!") 
indexes = which(check==c("!"))
ngram_3= ngram_3[-indexes,]


ngram_1$index<-seq(1:nrow(ngram_1))
ngram_2$index<-seq(1:nrow(ngram_2))
ngram_3$index<-seq(1:nrow(ngram_3))


uni<-glm(log(freq) ~ log(index), family="gaussian", data=ngram_1)
bi<-glm(log(freq) ~ log(index), family="gaussian", data=ngram_2)
tri<-glm(log(freq) ~ log(index), family="gaussian", data=ngram_3)

fit<-cbind(coef(uni), coef(bi), coef(tri))
print(fit)

library(ggplot2)
o = qplot(log(ngram_1$index),
      log(ngram_1$freq))+
  geom_abline(slope = fit[2, 1],
              intercept = fit[1, 1])+
  labs(x="Log(n-gram index)", y="Log(Number of freq)",
       title=sprintf("Double logarithmic plot of the Unigram occurrence distribution
                                                                     of %s",books_list[i,2]))
print(o)

index<-c(seq(1:10000), sample(seq(1:nrow(ngram_2)), 1000))
w = qplot(log(ngram_2$index), 
      log(ngram_2$freq))+
  geom_abline(slope = fit[2, 2],
              intercept = fit[1, 2])+
labs(x="Log(n-gram index)", y="Log(Number of freq)", title=sprintf("Double logarithmic plot of the bigram occurrence distribution
                                                                     of %s",books_list[i,2]))

print(w)

index<-c(seq(1:10000), sample(seq(1:nrow(ngram_3)), 1000))
c = qplot(log(ngram_3$index),
      log(ngram_3$freq))+
  geom_abline(slope = fit[2, 3],
              intercept = fit[1, 3])+
  labs(x="Log(n-gram index)", y="Log(Number of freq)", title=sprintf("Double logarithmic plot of the trigram occurrence distribution
                                                                     of %s",books_list[i,2]))

print(c)

check<-head(ngram_1, n=50)
x = qplot(check$ngrams,
      check$freq/sum(ngram_1$freq)) + 
  geom_bar(position = "dodge",
           stat="identity")+coord_flip() +
  scale_x_discrete(limits=check$ngrams[order(check$freq, 
                                         decreasing=F)])+labs(x="Unigram name",
                                                              y="Relative freq", title=sprintf("Unigram distribution of %s",
                                                                                               books_list[i,2])) 

print(x)

check<-head(ngram_2, n=50)
z = qplot(check$ngrams,
      check$freq/sum(ngram_2$freq)) + 
  geom_bar(position = "dodge",
           stat="identity")+coord_flip() +
  scale_x_discrete(limits=check$ngrams[order(check$freq, 
                                             decreasing=F)])+labs(x="Unigram name",
                                                                  y="Relative freq", title=sprintf("Bigram distribution of %s",
                                                                  books_list[i,2])) 
print(z)


check<-head(ngram_3, n=50)
q = qplot(check$ngrams,
      check$freq/sum(ngram_3$freq)) + 
  geom_bar(position = "dodge",
           stat="identity")+coord_flip() +
  scale_x_discrete(limits=check$ngrams[order(check$freq, 
                                             decreasing=F)])+labs(x="Unigram name",
                                                                  y="Relative freq", title=sprintf("Trigram distribution of %s",
                                                                                                   books_list[i,2])) 

print(q)










}
##                  [,1]       [,2]       [,3]
## (Intercept) 11.337486  6.7336969  3.0715170
## log(index)  -1.224877 -0.6375162 -0.2862819

##                  [,1]       [,2]      [,3]
## (Intercept) 10.313163  5.9551627  2.396489
## log(index)  -1.169316 -0.5919164 -0.235902

##                  [,1]       [,2]       [,3]
## (Intercept) 11.517407  6.9641867  3.0136661
## log(index)  -1.242178 -0.6525349 -0.2768444

##                 [,1]       [,2]      [,3]
## (Intercept) 10.88178  6.4141556  2.627028
## log(index)  -1.20301 -0.6166259 -0.248349

##                  [,1]       [,2]       [,3]
## (Intercept) 11.158596  6.6494959  2.7824117
## log(index)  -1.222728 -0.6327971 -0.2601516

##                  [,1]       [,2]       [,3]
## (Intercept) 11.478739  7.0608187  3.1845216
## log(index)  -1.237668 -0.6603593 -0.2912038

##                  [,1]       [,2]       [,3]
## (Intercept) 11.690652  7.1875756  3.2208653
## log(index)  -1.258168 -0.6696099 -0.2931969

##                  [,1]       [,2]       [,3]
## (Intercept) 11.722728  7.4793938  3.6324878
## log(index)  -1.276474 -0.7023451 -0.3320427

##                  [,1]      [,2]       [,3]
## (Intercept) 11.713911  7.282158  3.3609825
## log(index)  -1.262171 -0.676684 -0.3039053

##                  [,1]       [,2]       [,3]
## (Intercept)  9.324327  5.3676629  2.0068388
## log(index)  -1.076365 -0.5472623 -0.2030397

##                  [,1]      [,2]       [,3]
## (Intercept) 11.583040  7.171362  3.2721660
## log(index)  -1.249158 -0.668076 -0.2969449

##                  [,1]       [,2]      [,3]
## (Intercept)  9.975313  5.8303242  2.267838
## log(index)  -1.139437 -0.5835611 -0.224106

##                  [,1]       [,2]       [,3]
## (Intercept) 10.515060  6.5552689  2.9856149
## log(index)  -1.187739 -0.6427636 -0.2861696

##                  [,1]       [,2]       [,3]
## (Intercept) 11.392205  7.0771495  3.1872599
## log(index)  -1.230649 -0.6636513 -0.2919391

##                  [,1]       [,2]       [,3]
## (Intercept)  9.098942  5.1228686  1.8257404
## log(index)  -1.058781 -0.5280096 -0.1872684

Question 9:

We will do the same thing for Jane Eyre and Bronte’s works.

#HW8-q9:
#---------------------------
#Collectiing the book and cleaning it:
Jane_Eyre = gutenberg_download(1260)
Jane_Eyre[[2]][1:117] <- " " #removing contents page

Jane_Eyre_new = Jane_Eyre %>%  
  str_replace_all("\"","") %>% 
  str_replace_all("[[:punct:]]","") 
  
chapters =str_split(Jane_Eyre_new, "CHAPTER")
#---------------------------
# Extracting the 1-grams for every chapter:
r_2 =length(chapters[[2]])
ngram_dickens = list()
index = list()
for (i in 2:r_2)
{
  k = chapters[[2]][i] %>%
    str_to_lower() %>% 
    str_split(pattern = "\\s") %>% 
    unlist() %>% 
    table() %>% 
    as.data.frame(stringsAsFactors = F)
  colnames(k) = c("word","count")
  
  k = k %>%
    filter(!str_to_lower(word) %in% stop_words$word) %>% 
    filter(str_length(word)>1) %>% 
    filter(!str_detect(word,"\\d")) %>%
    arrange(desc(count)) %>% 
    mutate(proper = !word %in% str_to_lower(word))
  
  hc_chapter <- highchart() %>% 
    hc_add_series(name = "frequency of single words",data = k[1:50,],
                  type = "column",
                  mapping = hcaes(x = word, y = count),
                  color = "blue") %>% 
    hc_xAxis(title = list(text = "Name of the Words"),
             categories = k$word) %>% 
    hc_yAxis(title = list(text = "Frequency")) %>% 
    hc_title(text =sprintf("Frequency of words in Jane Eyre, Chapter %s"
                           ,i))
  
  
  d[[i]] = hc_chapter
  
}


#---------------------------
#Extracting 2-grams for every chapter:
ngram_dickens = list()
r_2 =length(chapters[[2]])
index = list()


for (i in 1:r_2)
{
  b = 0 
  ngram_dickens = chapters[[2]][i] %>%
    str_to_lower()
  ngram_dickens= get.phrasetable(ngram(ngram_dickens,n=2))
  ngram_dickens_dataframe_2= as.data.frame(ngram_dickens)
  
  
  
  hc_chapter_2 <- highchart() %>% 
    hc_add_series(name = "frequency of bigrams",data = ngram_dickens_dataframe_2[1:50,],
                  type = "column",
                  mapping = hcaes(x = ngrams, y = freq),
                  color = "blue") %>% 
    hc_xAxis(title = list(text = "Name of the Words"),
             categories = ngram_dickens_dataframe_2$ngrams) %>% 
    hc_yAxis(title = list(text = "Frequency")) %>% 
    hc_title(text =sprintf("Frequency of bigram in Jane Eyre, Chapter %s"
                           ,i))
  c[[i]] = hc_chapter_2
}




#---------------------------
htmltools::tagList(c)
htmltools::tagList(d)
#------
#ngram of charles Bronte books:
library(ngram)
book_bronte = gutenberg_works(str_detect(author, "Bronte"))
book_bronte %>% select(gutenberg_id,title) -> book_bronte

for (i in 1:3 )
{
  
  book_bronte_d =gutenberg_download(book_bronte[i,1])
  
  book_bronte_d = book_bronte_d %>%  
    str_replace_all("\"","") %>% 
    str_replace_all("[[:punct:]]","\\!!!!!!")
  book_bronte_d <- sapply(book_bronte_d,tolower)
  
  
  
  ng <-ngram(book_bronte_d,n=1)
  ngram_1 = get.phrasetable(ng)
  check = str_extract(ngram_1$ngrams,"\\!") 
  indexes = which(check==c("!"))
  ngram_1 = ngram_1[-indexes,]
  
  ng <-ngram(book_bronte_d,n=2)
  ngram_2 = get.phrasetable(ng)
  check = str_extract(ngram_2$ngrams,"\\!") 
  indexes = which(check==c("!"))
  ngram_2 = ngram_2[-indexes,]
  
  
  ng <-ngram(book_bronte_d,n=3)
  ngram_3 = get.phrasetable(ng)
  check = str_extract(ngram_3$ngrams,"\\!") 
  indexes = which(check==c("!"))
  ngram_3= ngram_3[-indexes,]
  
  
  ngram_1$index<-seq(1:nrow(ngram_1))
  ngram_2$index<-seq(1:nrow(ngram_2))
  ngram_3$index<-seq(1:nrow(ngram_3))
  
  
  uni<-glm(log(freq) ~ log(index), family="gaussian", data=ngram_1)
  bi<-glm(log(freq) ~ log(index), family="gaussian", data=ngram_2)
  tri<-glm(log(freq) ~ log(index), family="gaussian", data=ngram_3)
  
  fit<-cbind(coef(uni), coef(bi), coef(tri))
  print(fit)
  
  library(ggplot2)
w =  qplot(log(ngram_1$index),
        log(ngram_1$freq))+
    geom_abline(slope = fit[2, 1],
                intercept = fit[1, 1])+
    labs(x="Log(n-gram index)", y="Log(Number of freq)",
         title=sprintf("Double logarithmic plot of the Unigram occurrence distribution
                       of %s",book_bronte[i,2]))
 print(w) 
  
z =   qplot(log(ngram_2$index), 
        log(ngram_2$freq))+
    geom_abline(slope = fit[2, 2],
                intercept = fit[1, 2])+
    labs(x="Log(n-gram index)", y="Log(Number of freq)", title=sprintf("Double logarithmic plot of the bigram occurrence distribution
                                                                       of %s",book_bronte[i,2]))
  
print(z)

x =  qplot(log(ngram_3$index),
        log(ngram_3$freq))+
    geom_abline(slope = fit[2, 3],
                intercept = fit[1, 3])+
    labs(x="Log(n-gram index)", y="Log(Number of freq)", title=sprintf("Double logarithmic plot of the trigram occurrence distribution
                                                                       of %s",book_bronte[i,2]))
print(x
      )
  check<-head(ngram_1, n=50)
c=  qplot(check$ngrams,
        check$freq/sum(ngram_1$freq)) + 
    geom_bar(position = "dodge",
             stat="identity")+coord_flip() +
    scale_x_discrete(limits=check$ngrams[order(check$freq, 
                                               decreasing=F)])+labs(x="Unigram name",
                                                                    y="Relative freq", title=sprintf("Unigram distribution of %s",
                                                                                                     book_bronte[i,2])) 

print(c)
  
  check<-head(ngram_2, n=50)
v =  qplot(check$ngrams,
        check$freq/sum(ngram_2$freq)) + 
    geom_bar(position = "dodge",
             stat="identity")+coord_flip() +
    scale_x_discrete(limits=check$ngrams[order(check$freq, 
                                               decreasing=F)])+labs(x="Unigram name",
                                                                    y="Relative freq", title=sprintf("Bigram distribution of %s",
                                                                                                     book_bronte[i,2])) 
  print(v)
  
  
  check<-head(ngram_3, n=50)
m=  qplot(check$ngrams,
        check$freq/sum(ngram_3$freq)) + 
    geom_bar(position = "dodge",
             stat="identity")+coord_flip() +
    scale_x_discrete(limits=check$ngrams[order(check$freq, 
                                               decreasing=F)])+labs(x="Unigram name",
                                                                    y="Relative freq", title=sprintf("Trigram distribution of %s",
                                                                                                     book_bronte[i,2])) 
  
print(m)
  

  
}
##                   [,1]       [,2]       [,3]
## (Intercept)  7.3770649  3.2793818  0.8569237
## log(index)  -0.9755308 -0.3884393 -0.1031736

##                  [,1]       [,2]       [,3]
## (Intercept)  5.517735  2.2969672  0.7906645
## log(index)  -0.975414 -0.3663235 -0.1290325

##                   [,1]       [,2]       [,3]
## (Intercept)  5.2470129  2.0516256  0.6649229
## log(index)  -0.9273681 -0.3333573 -0.1116311

## Question 10: We can use the bigram,unigram and trigram of the works of Dickens and Bronte and use them as an independent variable. Using these values in different columns we can make a logistic model, but it’s been a very very long home-work and i’m way too tired for modeling this. So i may end this question with the given description of how we can make the logistic model.